**Replication File for Krupnikov, Style and Yontz, POQ
**Data is also posted via TESS

**Whether R assigned to Think or Feel in first question 
** firstqfeel 0=think 1=feel

**gender
**fem 1=woman 0 man

**partyid = 7 point
**1= strong dem, 7=strong rep
**Note: this should not be confused with PARTYID7, which is the panel level, untreated measure, unlreated to this study

**To match Burden coding, partyid is reverse coded
**partyid_rev, now 1= Strong Republican, 7=Strong Democrat 

**Analyses
**Table 1 Patterns
**Think Patterns
tab partyid_rev if fem==1&firstqfeel==0 
mean partyid_rev if fem==1&firstqfeel==0 
tab partyid_rev if fem==0&firstqfeel==0 
mean partyid_rev if fem==0&firstqfeel==0 

**Differences by gender, within treatment
ttest partyid_rev if firstqfeel==0, by(fem)

**Feel Patterns
tab partyid_rev if fem==1&firstqfeel==1
mean partyid_rev if fem==1&firstqfeel==1 
tab partyid_rev if fem==0&firstqfeel==1 
mean partyid_rev if fem==0&firstqfeel==1 

**Differences by gender, within treatment
ttest partyid_rev if firstqfeel==1, by(fem)

**Differences by treatment, within gender
ttest partyid_rev if fem==1, by(firstqfeel)
ttest partyid_rev if fem==0, by(firstqfeel)

**Chi Squared tests, by gender, within treatment
tab partyid_rev fem if firstqfeel==0, chi2
tab partyid_rev fem if firstqfeel==1, chi2

**Remainder of results in Table 1 taken directly from Burden (2008) Table 1

**Table 2
**generate 3 category party ID
**partyid3 1 = Rep (including lean), 2 = Pure Ind, 3 = Dem (including lean)

**Analyses
**Women
tab partyid3 if fem==1&firstqfeel==0
tab partyid3 if fem==1&firstqfeel==1
tab partyid3 firstqfeel if fem==1, chi2

**Men
tab partyid3 if fem==0&firstqfeel==0
tab partyid3 if fem==0&firstqfeel==1
tab partyid3 firstqfeel if fem==0, chi2

**Remainders of results in Table 2 taken directly from Burden (2008) Table 2

**Figure 1
**Results are based on calculations of the gender gap as described in the paper
**Raw results obtained via code below, gap calculated outside of stata. Figures created in R.

**Main covariates for analyses:
**measures of sophistication
**anycollege 0 no college, 1 some/BA, panel-levl measure provided by NORC

**attention to news, panel-level measure provided by NORC
**newsinfo3 0= low, 1 = medium 2=high

**interest in pol, panel-level measure provided by NORC
**interest3 0=low, 1=medium, 2=high

**Components of the gap measure were calculated as follows:
**Education
tab partyid3 if firstqfeel==0&fem==1&anycollege==1
tab partyid3 if firstqfeel==0&fem==0&anycollege==1
tab partyid3 if firstqfeel==1&fem==1&anycollege==1
tab partyid3 if firstqfeel==1&fem==0&anycollege==1

tab partyid3 if firstqfeel==0&fem==1&anycollege==0
tab partyid3 if firstqfeel==0&fem==0&anycollege==0
tab partyid3 if firstqfeel==1&fem==1&anycollege==0
tab partyid3 if firstqfeel==1&fem==0&anycollege==0

**News Attention
tab partyid3 if firstqfeel==0&fem==1&newsinfo3==2
tab partyid3 if firstqfeel==0&fem==0&newsinfo3==2
tab partyid3 fem if firstqfeel==0&newsinfo3==2, chi2
tab partyid3 if firstqfeel==1&fem==1&newsinfo3==2
tab partyid3 if firstqfeel==1&fem==0&newsinfo3==2
tab partyid3 fem if firstqfeel==1&newsinfo3==2, chi2


tab partyid3 if firstqfeel==0&fem==1&newsinfo3==0
tab partyid3 if firstqfeel==0&fem==0&newsinfo3==0
tab partyid3 fem if firstqfeel==0&newsinfo3==0, chi2

tab partyid3 if firstqfeel==1&fem==1&newsinfo3==0
tab partyid3 if firstqfeel==1&fem==0&newsinfo3==0
tab partyid3 fem if firstqfeel==1&newsinfo3==0, chi2

**Middle category in appendix only
tab partyid3 if firstqfeel==0&fem==1&newsinfo3==1
tab partyid3 if firstqfeel==0&fem==0&newsinfo3==1
tab partyid3 if firstqfeel==1&fem==1&newsinfo3==1
tab partyid3 if firstqfeel==1&fem==0&newsinfo3==1

**Interest
tab partyid3 if firstqfeel==0&fem==1&interest3==2
tab partyid3 if firstqfeel==0&fem==0&interest3==2
tab partyid3 fem if firstqfeel==0&interest3==2, chi2

tab partyid3 if firstqfeel==1&fem==1&interest3==2
tab partyid3 if firstqfeel==1&fem==0&interest3==2
tab partyid3 fem if firstqfeel==1&interest3==2, chi2


tab partyid3 if firstqfeel==0&fem==1&interest3==0
tab partyid3 if firstqfeel==0&fem==0&interest3==0
tab partyid3 if firstqfeel==1&fem==1&interest3==0
tab partyid3 if firstqfeel==1&fem==0&interest3==0

**Middle category in appendix only

tab partyid3 if firstqfeel==0&fem==1&interest3==1
tab partyid3 if firstqfeel==0&fem==0&interest3==1
tab partyid3 if firstqfeel==1&fem==1&interest3==1
tab partyid3 if firstqfeel==1&fem==0&interest3==1


**Figure 2

**models
reg partyid_rev c.firstqfeel##c.fem##c.anycollege
margins, dydx(fem) at(firstqfeel=(0) anycollege=(1)) post
estimates store educhigh

reg partyid_rev c.firstqfeel##c.fem##c.anycollege
margins, dydx(fem) at(firstqfeel=(1) anycollege=(1)) post
estimates store educhigh1

reg partyid_rev c.firstqfeel##c.fem##c.newsinfo3
margins, dydx(fem) at(firstqfeel=(0) newsinfo3=(2)) post
estimates store newshigh

reg partyid_rev c.firstqfeel##c.fem##c.newsinfo3
margins, dydx(fem) at(firstqfeel=(1) newsinfo3=(2)) post
estimates store newshigh1

reg partyid_rev c.firstqfeel##c.fem##c.interest3
margins, dydx(fem) at(firstqfeel=(0) interest3=(2)) post
estimates store interesthigh

reg partyid_rev c.firstqfeel##c.fem##c.interest3
margins, dydx(fem) at(firstqfeel=(1) interest3=(2)) post
estimates store interesthigh1

set scheme plotplain

coefplot (educhigh, offset (-0.4)) (educhigh1, offset (-0.3)) (newshigh, offset (-0.05)) (newshigh1, offset (0.05)) (interesthigh, offset (0.3)) (interesthigh1, offset (0.4)), grid(none) byopts(compact cols(1)) level(95) xsize(3.5) vertical yscale(range(2 -2)) ylabel(2 (0.5) -2) ytitle("Difference between men and women") xlabel(0.6 "High Educ." 1 "High News" 1.4 "High Interest")

**note: figure then edited to clarify legend


reg partyid_rev c.firstqfeel##c.fem##c.anycollege
margins, dydx(fem) at(firstqfeel=(0) anycollege=(0)) post
estimates store educlow

reg partyid_rev c.firstqfeel##c.fem##c.anycollege
margins, dydx(fem) at(firstqfeel=(1) anycollege=(0)) post
estimates store educlow1

reg partyid_rev c.firstqfeel##c.fem##c.newsinfo3
margins, dydx(fem) at(firstqfeel=(0) newsinfo3=(0)) post
estimates store newslow

reg partyid_rev c.firstqfeel##c.fem##c.newsinfo3
margins, dydx(fem) at(firstqfeel=(1) newsinfo3=(0)) post
estimates store newslow1

reg partyid_rev c.firstqfeel##c.fem##c.interest3
margins, dydx(fem) at(firstqfeel=(0) interest3=(0)) post
estimates store interestlow

reg partyid_rev c.firstqfeel##c.fem##c.interest3
margins, dydx(fem) at(firstqfeel=(1) interest3=(0)) post
estimates store interestlow1

coefplot (educlow, offset (-0.4)) (educlow1, offset (-0.3)) (newslow, offset (-0.05)) (newslow1, offset (0.05)) (interestlow, offset (0.3)) (interestlow1, offset (0.4)), grid(none) byopts(compact cols(1)) level(95) xsize(3.5) vertical yscale(range(2 -2)) ylabel(2 (0.5) -2) ytitle("Difference between men and women") xlabel(0.6 "Low Educ" 1 "Low News" 1.4 "Low Interest")

**Independent Results
gen leaners=partyid_rev
recode leaners 1/2=0 3/5=1 6/7=0
**In-text mentions from results in SI 7.3.1 and 7.3.2
logit leaners c.firstqfeel##c.fem
margins, dydx(firstqfeel) at (fem = (0 1))
margins, dydx(fem) at (firstqfeel = (0 1))
logit leaners c.firstqfeel##c.fem age married income ideo2

reg leaners c.firstqfeel##c.fem
margins, dydx(firstqfeel) at (fem = (0 1))
margins, dydx(fem) at (firstqfeel = (0 1))
reg leaners c.firstqfeel##c.fem age married income ideo2



**Appendix only results, model with controls
**SI Table 7.2.1
reg partyid_rev c.firstqfeel##c.fem##c.anycollege age married income ideo2
reg partyid_rev c.firstqfeel##c.fem##c.newsinfo3 age married income ideo2
reg partyid_rev c.firstqfeel##c.fem##c.interest3 age married income ideo2

**SI Tables 7.2.2 and 7.2.3
**The three-category sophistication variables are recoded into two categories -- 2 lowest categories merged.
reg partyid_rev c.firstqfeel##c.fem if anycollege==1
margins, dydx(firstqfeel) at(fem=(0 1))
reg partyid_rev c.firstqfeel##c.fem if newsinfo3==2
margins, dydx(firstqfeel) at(fem=(0 1))
reg partyid_rev c.firstqfeel##c.fem if interest3==2
margins, dydx(firstqfeel) at(fem=(0 1))

**SI 7.2.5
reg partyid_rev c.firstqfeel##c.fem##c.newsinfo2

reg partyid_rev c.firstqfeel##c.fem##c.interest2

**Figure 7.2.1
reg partyid_rev c.firstqfeel##c.fem##c.newsinfo2
margins, dydx(fem) at(firstqfeel=(0) newsinfo2=(0)) post
estimates store newslowmed

reg partyid_rev c.firstqfeel##c.fem##c.newsinfo2
margins, dydx(fem) at(firstqfeel=(1) newsinfo2=(0)) post
estimates store newslowmed1

reg partyid_rev c.firstqfeel##c.fem##c.interest2
margins, dydx(fem) at(firstqfeel=(0) interest=(0)) post
estimates store intlowmed

reg partyid_rev c.firstqfeel##c.fem##c.interest2
margins, dydx(fem) at(firstqfeel=(1) interest=(0)) post
estimates store intlowmed1


coefplot (educlow, offset (-0.4)) (educlow1, offset (-0.3)) (newslowmed, offset (-0.05)) (newslowmed1, offset (0.05)) (intlowmed, offset (0.3)) (intlowmed1, offset (0.4)), grid(none) byopts(compact cols(1)) level(95) xsize(3.5) vertical yscale(range(2 -2)) ylabel(2 (0.5) -2) ytitle("Difference between men and women") xlabel(0.6 "Low Educ" 1 "Low/Med News" 1.4 "Low/Med Interest")



**SI 7.3 is indepenent results (above)

**SI 7.4 Partisan Importance 
**Importance by condition
**lower values mean the party is more important 
mean partyimport if FEEL_THINK==1&fem==1 //Think, Think
mean partyimport if FEEL_THINK==3&fem==1 //Feel, Feel 
mean partyimport if FEEL_THINK==4&fem==1 //Feel, Think
mean partyimport if FEEL_THINK==2&fem==1 //Think, Feel

**Table SI 7.4.1
regress partyimport i.FEEL_THINK if fem==1

**SI 8 Robustness Checks
**Tables 8.1.1 and 8.1.2
**order 1 this study first, 0 otherwise 
reg partyid_rev c.firstqfeel##c.fem##c.order
margins, dydx(firstqfeel) at(fem=(1 0) order=(1))
margins, dydx(firstqfeel) at(fem=(1 0) order=(0))

**Table 8.2.1
** absolute value of compare is the variable comparing the panel and post-treatment PIDs
tab compare_abs if firstqfeel==0&fem==1
tab compare_abs if firstqfeel==1&fem==1
tab compare_abs if firstqfeel==0&fem==0
tab compare_abs if firstqfeel==1&fem==0

**Table 8.2.2.
**Note,  for this measure we use a party id measure where strong Republican is 7 (so, different from rest of the paper)

reg compare c.firstqfeel##c.fem
margins, dydx(firstqfeel) at (fem = (0 1))

**Comp_strength compares strength of post-treat and panel-level measure
reg comp_strength c.firstqfeel##c.fem
margins, dydx(firstqfeel) at (fem = (0 1))


**Figure 8.2.1.
**Again party is 7 for strong Republican, in the same direction as ideology
reg partyid c.firstqfeel##c.fem##c.ideo2
margins, dydx(firstqfeel) at(fem=(0 1) ideo2=(1(1)7))
marginsplot, by(fem)

**Table 8.3.1
reg partyid_rev c.firstqfeel##c.fem##c.age
reg partyid_rev c.firstqfeel##c.fem##c.age6

**Figure 8.4.1
 reg partyid c.firstqfeel##c.fem##c.gender_id1
margins, dydx(firstqfeel) at(fem=(0 1) gender_id1=(1(1)5))
 marginsplot, by(fem)
 
  reg partyid c.firstqfeel##c.fem##c.gender_id2
margins, dydx(firstqfeel) at(fem=(0 1) gender_id2=(1(1)5))
 marginsplot, by(fem)
 
 **SI 9
 **This section considers the comparison with Burden using the NORC weights. 
 **This replicates the analyses with the weight variable = weight 












